Abstract
A surge of interest in data-intensive computing has led to a drastic increase in the demand for data centers. Given this growing popularity, data centers are becoming a primary contributor to the increased consumption of energy worldwide. To mitigate this problem, this paper revisits DVFS (Dynamic Voltage Frequency Scaling), a well-known technique to reduce the energy usage of processors, from the viewpoint of distributed systems. Distributed data systems typically adopt a replication facility to provide high availability and short latency. In this type of architecture, the replicas are maintained in an asynchronous manner, while the master synchronously operates via user requests. Based on this relaxation constraint of replica, we present a novel DVFS technique called Concerto, which intentionally scales down the frequency of processors operating for the replicas. This mechanism can achieve considerable energy savings without an increase in the user-perceived latency. We implemented Concerto on Redis 6.0.1, a commercial-level distributed key-value store, demonstrating that all associated performance issues were resolved. To prevent a delay in read queries assigned to the replicas, we offload the independent part of the read operation to the fast-running thread. We also empirically demonstrate that the decreased performance of the replica does not cause an increase of the replication lag because the inherent load unbalance between the master and replica hides the increased latency of the replica. Performance evaluations with micro and real-world benchmarks show that Redis saves 32% on average and up to 51% of energy with Concerto under various workloads, with minor performance losses in the replicas. Despite numerous studies of the energy saving in data centers, to the best of our best knowledge, Concerto is the first approach that considers clock-speed scaling at the aggregate level, exploiting heterogeneous performance constraints across data nodes.
Highlights
Data centers play a pivotal role in this age of big data
Virtualization technologies have led to a large reduction in energy use by reducing the number of servers operating in data centers [1], and advanced power management schemes for processors have contributed to less energy per computation [2]
Energy efficiency continues to be important for data centers; in the U.S, the electricity used by data centers accounted for 1.3% of worldwide energy production in 2010, and these centers generate more than 43 million tons of CO2 emissions per year, equal to 2% of the worldwide figure
Summary
As more applications depend on data-intensive technologies, such as artificial intelligence, bioinformatics, and data analytics, the demand for data centers continues to increase Despite this increase in popularity, the energy use per computation in data centers has decreased significantly over the past decade. This promising shift stems from the energy efficiency progress that has been made during this time. Virtualization technologies have led to a large reduction in energy use by reducing the number of servers operating in data centers [1], and advanced power management schemes for processors have contributed to less energy per computation [2]. We briefly explain the power-management technique adopted in Intel processors, our target architecture (Section 2.1.1), and the linux CPU frequency governors that enable one to set the desired frequency over the processors (Section 2.1.2)
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